{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "transfer_learning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhyPjzWq_Xb1",
        "colab_type": "text"
      },
      "source": [
        "## Transfer Learning Design Pattern\n",
        "\n",
        "In Transfer Learning, we take part of a previously trained model, freeze the weights, and incorporate these non-trainable layers into a new model that solves a similar problem, but on a smaller dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NikTzWZr_cxP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_hub as hub\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FxRIJ0kgbj4",
        "colab_type": "text"
      },
      "source": [
        "### Building a medical imaging classification model with Keras and VGG\n",
        "\n",
        "We'll use a [colorectal histology](https://www.tensorflow.org/datasets/catalog/colorectal_histology) dataset that comes pre-installed with TF Datasets. First we'll extract the data as a TF Dataset. Then we'll load a VGG model without the top classification layers. Finally, we'll add layers specific to our classification problem so that the final model outputs a softmax classification corresponding with 1 of the 8 classes in our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smSfugBIztMi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# These images will be (150,150,3)\n",
        "(train, validation, test), info = tfds.load(\n",
        "    'colorectal_histology',\n",
        "    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'], \n",
        "    shuffle_files=True, \n",
        "    as_supervised=True,\n",
        "    with_info=True\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puVkJrwGt7vZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "c920d609-3fcc-4195-a7b4-0284ea229010"
      },
      "source": [
        "# Some info on the original dataset\n",
        "info"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='colorectal_histology',\n",
              "    version=2.0.0,\n",
              "    description='Classification of textures in colorectal cancer histology. Each example is a 150 x 150 x 3 RGB image of one of 8 classes.',\n",
              "    homepage='https://zenodo.org/record/53169#.XGZemKwzbmG',\n",
              "    features=FeaturesDict({\n",
              "        'filename': Text(shape=(), dtype=tf.string),\n",
              "        'image': Image(shape=(150, 150, 3), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),\n",
              "    }),\n",
              "    total_num_examples=5000,\n",
              "    splits={\n",
              "        'train': 5000,\n",
              "    },\n",
              "    supervised_keys=('image', 'label'),\n",
              "    citation=\"\"\"@article{kather2016multi,\n",
              "      title={Multi-class texture analysis in colorectal cancer histology},\n",
              "      author={Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Z{\"o}llner, Frank Gerrit},\n",
              "      journal={Scientific reports},\n",
              "      volume={6},\n",
              "      pages={27988},\n",
              "      year={2016},\n",
              "      publisher={Nature Publishing Group}\n",
              "    }\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2B65x4euAGs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Utility for what each label corresponds with\n",
        "label_map = ['tumor','stroma','complex','lympho','debris','mucosa', 'adipose']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1sk8GxDsLlU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "80838306-50bc-491f-f8a9-27697fd4a0b0"
      },
      "source": [
        "# Preview 2 examples from our dataset\n",
        "get_label_name = info.features['label'].int2str\n",
        "for image, label in train.take(2):\n",
        "  plt.figure()\n",
        "  plt.imshow(np.array(image))\n",
        "  plt.title(get_label_name(label))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RosKjhd7Mw5D",
        "colab_type": "text"
      },
      "source": [
        "The labels in the original dataset are single scalar values (ranging from 0 to 7). We need to convert these to softmax arrays to train our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwV9urMgzoES",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def label_format(image, label):\n",
        "  return (image, tf.one_hot(label, depth=8))\n",
        "\n",
        "train = train.map(label_format)\n",
        "validation = validation.map(label_format)\n",
        "test = test.map(label_format)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXfbz5M_ztVg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create batches\n",
        "train_batch = train.shuffle(500).batch(32)\n",
        "val_batch = validation.batch(32)\n",
        "test_batch = test.batch(32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLAFfWmvyPTD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b26782b-88fa-4e82-cf89-9c9ad5664d40"
      },
      "source": [
        "for image_batch, label_batch in train_batch.take(1):\n",
        "   pass\n",
        "\n",
        "image_batch.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([32, 150, 150, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGC5bFmh_iEy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the VGG model and set trainable to false\n",
        "vgg_model = tf.keras.applications.VGG19(\n",
        "    include_top=False, \n",
        "    weights='imagenet', \n",
        "    input_shape=((150,150,3)), \n",
        "    classifier_activation='softmax'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57uCPtloyFVf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vgg_model.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dD7m5_7KAly6",
        "colab_type": "code",
        "outputId": "a4acd0cb-d9c9-4ac5-c7a9-dfd429636993",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "vgg_model.summary()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg19\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_6 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "=================================================================\n",
            "Total params: 20,024,384\n",
            "Trainable params: 0\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRrkSXH1ymCD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_batch = vgg_model(image_batch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lR3twN4yery",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "95b978f1-db74-4365-ba1d-7283cc6ca844"
      },
      "source": [
        "global_avg_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_avg = global_avg_layer(feature_batch)\n",
        "print(feature_batch_avg.shape)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eEi64ktym0H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "519ec217-d417-40c2-89a1-e87acc724a73"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(8, activation='softmax')\n",
        "prediction_batch = prediction_layer(feature_batch_avg)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKLOaeKpyIaK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build our new model, implementing transfer learning\n",
        "colorectal_model = keras.Sequential([\n",
        "  vgg_model,\n",
        "  global_avg_layer,\n",
        "  prediction_layer\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vbaj9fz5zUGk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "91773daf-ff74-4d95-eb54-658ebeac4e0e"
      },
      "source": [
        "colorectal_model.summary()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg19 (Model)                (None, 4, 4, 512)         20024384  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 8)                 4104      \n",
            "=================================================================\n",
            "Total params: 20,028,488\n",
            "Trainable params: 4,104\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3E1Lkv4y3tY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "colorectal_model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEDEfwaJy3wL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "10e291f6-879e-46c3-c92c-b81ea6b6a113"
      },
      "source": [
        "colorectal_model.fit(\n",
        "    train_batch,\n",
        "    validation_data=val_batch,\n",
        "    epochs=15\n",
        ")"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "125/125 [==============================] - 14s 109ms/step - loss: 1.8300 - accuracy: 0.4392 - val_loss: 1.7325 - val_accuracy: 0.5360\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 14s 109ms/step - loss: 1.6779 - accuracy: 0.5955 - val_loss: 1.6425 - val_accuracy: 0.6260\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 14s 110ms/step - loss: 1.6087 - accuracy: 0.6653 - val_loss: 1.5841 - val_accuracy: 0.6960\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 14s 110ms/step - loss: 1.5628 - accuracy: 0.7138 - val_loss: 1.5512 - val_accuracy: 0.7240\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 14s 111ms/step - loss: 1.5414 - accuracy: 0.7343 - val_loss: 1.5336 - val_accuracy: 0.7400\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 14s 111ms/step - loss: 1.5256 - accuracy: 0.7527 - val_loss: 1.5252 - val_accuracy: 0.7480\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 14s 112ms/step - loss: 1.5163 - accuracy: 0.7605 - val_loss: 1.5237 - val_accuracy: 0.7500\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 1.5062 - accuracy: 0.7710 - val_loss: 1.5198 - val_accuracy: 0.7540\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 1.5008 - accuracy: 0.7770 - val_loss: 1.5159 - val_accuracy: 0.7580\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 1.4973 - accuracy: 0.7800 - val_loss: 1.5146 - val_accuracy: 0.7600\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 1.4907 - accuracy: 0.7897 - val_loss: 1.5153 - val_accuracy: 0.7660\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 1.4842 - accuracy: 0.7955 - val_loss: 1.5138 - val_accuracy: 0.7600\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 1.4848 - accuracy: 0.7947 - val_loss: 1.5113 - val_accuracy: 0.7660\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 14s 116ms/step - loss: 1.4797 - accuracy: 0.8005 - val_loss: 1.5113 - val_accuracy: 0.7660\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 1.4763 - accuracy: 0.8020 - val_loss: 1.5124 - val_accuracy: 0.7620\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f183f413f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pzwo1SRsRzEO",
        "colab_type": "text"
      },
      "source": [
        "### Building a text classification model with TF Hub\n",
        "\n",
        "We'll use the [IMDB movie review dataset](https://www.tensorflow.org/datasets/catalog/imdb_reviews) from TF Datasets. This contains 50k movie reviews with polarized sentiment. The goal is to train a model to predict whether a review is positive or negative. We'll use TF Hub to build the first layer of our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiAG_MFig9Wy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the data and split into train, test, validate\n",
        "reviews_train, reviews_validate, reviews_test = tfds.load(\n",
        "    'imdb_reviews', \n",
        "    split=('train[:80%]', 'train[80%:90%]', 'test'),\n",
        "    as_supervised=True\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkYV9_NjY9Bd",
        "colab_type": "text"
      },
      "source": [
        "Since this is already formatted as a `tf.Data.dataset`, we'll preview it by iterating over the first five examples. You should see the review and its corresponding rating. 0 is negative, 1 is positive. When we built our model, we'll use sigmoid as the output since this is a binary classification task."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjdFNbtlUPfh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "0d22e40f-3413-44a3-e415-1601121b6d2b"
      },
      "source": [
        "for i in reviews_train.take(5):\n",
        "  print('Review text', i[0].numpy())\n",
        "  print('Review sentiment', i[1].numpy(), '\\n')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Review text b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\"\n",
            "Review sentiment 0 \n",
            "\n",
            "Review text b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.'\n",
            "Review sentiment 0 \n",
            "\n",
            "Review text b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.'\n",
            "Review sentiment 0 \n",
            "\n",
            "Review text b'This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.'\n",
            "Review sentiment 1 \n",
            "\n",
            "Review text b'As others have mentioned, all the women that go nude in this film are mostly absolutely gorgeous. The plot very ably shows the hypocrisy of the female libido. When men are around they want to be pursued, but when no \"men\" are around, they become the pursuers of a 14 year old boy. And the boy becomes a man really fast (we should all be so lucky at this age!). He then gets up the courage to pursue his true love.'\n",
            "Review sentiment 1 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QnlohWMZvYV",
        "colab_type": "text"
      },
      "source": [
        "Here we'll import a TF Hub module for text classification. Because we're using TF Hub, we can feed the data to our model directly as strings. We don't need to worry about preprocessing since TF Hub will handle converting the text to embeddings for us."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG7IH_url883",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hub_layer = hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\",\n",
        "                           input_shape=[], dtype=tf.string, trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQRy_BgybQFc",
        "colab_type": "text"
      },
      "source": [
        "To see what the TF Hub layer is doing, let's see the embedding for an example sentence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeO9-92XbVq-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "cc92e394-d4de-422b-ea9a-7d53774a204a"
      },
      "source": [
        "test_embedding = hub_layer([\"I'm excited to try out transfer learning with TF Hub\"])\n",
        "print(test_embedding)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[ 0.3780208  -0.8602792   0.47116035  0.87798494 -1.6212037  -0.93068135\n",
            "  -0.15095684  0.24388756 -0.6550514   0.12114644 -1.3243198   0.73917985\n",
            "   0.52290976  0.6004198  -1.0018169   0.27700785  1.8433737   0.11805207\n",
            "  -0.1830085  -0.6638955 ]], shape=(1, 20), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGzKPwDbl9Rv",
        "colab_type": "code",
        "outputId": "c170ac70-9f29-40b3-e3b6-f61cf41a9ed1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "model = keras.Sequential([\n",
        "  hub_layer,\n",
        "  keras.layers.Dense(32, activation='relu'),\n",
        "  keras.layers.Dense(1, activation='sigmoid')                          \n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_2 (KerasLayer)   (None, 20)                400020    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 32)                672       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 400,725\n",
            "Trainable params: 400,725\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdDvNGRnTyrJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuQ4EYT1oo2J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "d1eb88c4-e8f0-49c8-fb6d-adb0ebc8ef1b"
      },
      "source": [
        "# Note: you can train for more epochs to get higher accuracy\n",
        "model.fit(\n",
        "    reviews_train.shuffle(10000).batch(512), \n",
        "    validation_data=reviews_validate.batch(512), \n",
        "    epochs=15\n",
        ")"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "40/40 [==============================] - 4s 91ms/step - loss: 0.6748 - accuracy: 0.5717 - val_loss: 0.6563 - val_accuracy: 0.6228\n",
            "Epoch 2/15\n",
            "40/40 [==============================] - 3s 81ms/step - loss: 0.6539 - accuracy: 0.6571 - val_loss: 0.6410 - val_accuracy: 0.6684\n",
            "Epoch 3/15\n",
            "40/40 [==============================] - 4s 95ms/step - loss: 0.6380 - accuracy: 0.7063 - val_loss: 0.6244 - val_accuracy: 0.7284\n",
            "Epoch 4/15\n",
            "40/40 [==============================] - 4s 89ms/step - loss: 0.6209 - accuracy: 0.7558 - val_loss: 0.6104 - val_accuracy: 0.7648\n",
            "Epoch 5/15\n",
            "40/40 [==============================] - 4s 90ms/step - loss: 0.6063 - accuracy: 0.7919 - val_loss: 0.5997 - val_accuracy: 0.8040\n",
            "Epoch 6/15\n",
            "40/40 [==============================] - 4s 89ms/step - loss: 0.5946 - accuracy: 0.8185 - val_loss: 0.5903 - val_accuracy: 0.8120\n",
            "Epoch 7/15\n",
            "40/40 [==============================] - 4s 88ms/step - loss: 0.5841 - accuracy: 0.8413 - val_loss: 0.5834 - val_accuracy: 0.8332\n",
            "Epoch 8/15\n",
            "40/40 [==============================] - 4s 91ms/step - loss: 0.5761 - accuracy: 0.8586 - val_loss: 0.5793 - val_accuracy: 0.8252\n",
            "Epoch 9/15\n",
            "40/40 [==============================] - 4s 91ms/step - loss: 0.5702 - accuracy: 0.8672 - val_loss: 0.5747 - val_accuracy: 0.8480\n",
            "Epoch 10/15\n",
            "40/40 [==============================] - 4s 99ms/step - loss: 0.5641 - accuracy: 0.8832 - val_loss: 0.5714 - val_accuracy: 0.8536\n",
            "Epoch 11/15\n",
            "40/40 [==============================] - 4s 108ms/step - loss: 0.5594 - accuracy: 0.8938 - val_loss: 0.5688 - val_accuracy: 0.8568\n",
            "Epoch 12/15\n",
            "40/40 [==============================] - 4s 109ms/step - loss: 0.5553 - accuracy: 0.9011 - val_loss: 0.5671 - val_accuracy: 0.8644\n",
            "Epoch 13/15\n",
            "40/40 [==============================] - 4s 106ms/step - loss: 0.5518 - accuracy: 0.9093 - val_loss: 0.5657 - val_accuracy: 0.8668\n",
            "Epoch 14/15\n",
            "40/40 [==============================] - 4s 108ms/step - loss: 0.5485 - accuracy: 0.9166 - val_loss: 0.5639 - val_accuracy: 0.8680\n",
            "Epoch 15/15\n",
            "40/40 [==============================] - 4s 111ms/step - loss: 0.5456 - accuracy: 0.9212 - val_loss: 0.5644 - val_accuracy: 0.8696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff2a2a77a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07uyqIuOXshL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92767d9d-63af-45c9-9bdb-d87fd8e62305"
      },
      "source": [
        "# Evaluate the model\n",
        "results = model.evaluate(reviews_test.batch(512))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49/49 [==============================] - 4s 77ms/step - loss: 0.5705 - accuracy: 0.8641\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1cVZ1gn1c2Fn",
        "colab_type": "text"
      },
      "source": [
        "Here we'll generate predictions on example from our test dataset, and print out the first 10 predictions along with their corresponding review text. \n",
        "\n",
        "The output of our model is a value between 0 and 1. Values close to 0 indicate a confident negative review, and values close to 1 indicate a confident positive review."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtfLY1mNTx58",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = model.predict(reviews_test.batch(512))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SghuuvU0X2LK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "7787b78a-b67a-4b0c-a856-3cd26bf88d2b"
      },
      "source": [
        "for i,val in enumerate(reviews_test.take(10)):\n",
        "  print(val[0])\n",
        "  print(prediction[i][0])\n",
        "  print()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(b\"There are films that make careers. For George Romero, it was NIGHT OF THE LIVING DEAD; for Kevin Smith, CLERKS; for Robert Rodriguez, EL MARIACHI. Add to that list Onur Tukel's absolutely amazing DING-A-LING-LESS. Flawless film-making, and as assured and as professional as any of the aforementioned movies. I haven't laughed this hard since I saw THE FULL MONTY. (And, even then, I don't think I laughed quite this hard... So to speak.) Tukel's talent is considerable: DING-A-LING-LESS is so chock full of double entendres that one would have to sit down with a copy of this script and do a line-by-line examination of it to fully appreciate the, uh, breadth and width of it. Every shot is beautifully composed (a clear sign of a sure-handed director), and the performances all around are solid (there's none of the over-the-top scenery chewing one might've expected from a film like this). DING-A-LING-LESS is a film whose time has come.\", shape=(), dtype=string)\n",
            "0.43071708\n",
            "\n",
            "tf.Tensor(b\"A blackly comic tale of a down-trodden priest, Nazarin showcases the economy that Luis Bunuel was able to achieve in being able to tell a deeply humanist fable with a minimum of fuss. As an output from his Mexican era of film making, it was an invaluable talent to possess, with little money and extremely tight schedules. Nazarin, however, surpasses many of Bunuel's previous Mexican films in terms of the acting (Francisco Rabal is excellent), narrative and theme.<br /><br />The theme, interestingly, is something that was explored again in Viridiana, made three years later in Spain. It concerns the individual's struggle for humanity and altruism amongst a society that rejects any notion of virtue. Father Nazarin, however, is portrayed more sympathetically than Sister Viridiana. Whereas the latter seems to choose charity because she wishes to atone for her (perceived) sins, Nazarin's whole existence and reason for being seems to be to help others, whether they (or we) like it or not. The film's last scenes, in which he casts doubt on his behaviour and, in a split second, has to choose between the life he has been leading or the conventional life that is expected of a priest, are so emotional because they concern his moral integrity and we are never quite sure whether it remains intact or not.<br /><br />This is a remarkable film and I would urge anyone interested in classic cinema to seek it out. It is one of Bunuel's most moving films, and encapsulates many of his obsessions: frustrated desire, mad love, religious hypocrisy etc. In my view 'Nazarin' is second only to 'The Exterminating Angel', in terms of his Mexican movies, and is certainly near the top of the list of Bunuel's total filmic output.\", shape=(), dtype=string)\n",
            "0.521048\n",
            "\n",
            "tf.Tensor(b'Scary Movie 1-4, Epic Movie, Date Movie, Meet the Spartans, Not another Teen Movie and Another Gay Movie. Making \"Superhero Movie\" the eleventh in a series that single handily ruined the parody genre. Now I\\'ll admit it I have a soft spot for classics such as Airplane and The Naked Gun but you know you\\'ve milked a franchise so bad when you can see the gags a mile off. In fact the only thing that might really temp you into going to see this disaster is the incredibly funny but massive sell-out Leslie Neilson.<br /><br />You can tell he needs the money, wither that or he intends to go down with the ship like a good Capitan would. In no way is he bringing down this genre but hell he\\'s not helping it. But if I feel sorry for anybody in this film its decent actor Drake Bell who is put through an immense amount of embarrassment. The people who are put through the largest amount of torture by far however is the audience forced to sit through 90 minutes of laughless bile no funnier than herpes.<br /><br />After spoofing disaster films in Airplane!, police shows in The Naked Gun, and Hollywood horrors in Scary Movie 3 and 4, producer David Zucker sets his satirical sights on the superhero genre with this anarchic comedy lampooning everything from Spider-Man to X-Men and Superman Returns.<br /><br />Shortly after being bitten by a genetically altered dragonfly, high-school outcast Rick Riker (Drake Bell) begins to experience a startling transformation. Now Rick\\'s skin is as strong as steel, and he possesses the strength of ten men. Determined to use his newfound powers to fight crime, Rick creates a special costume and assumes the identity of The Dragonfly -- a fearless crime fighter dedicated to keeping the streets safe for law-abiding citizens.<br /><br />But every superhero needs a nemesis, and after Lou Landers (Christopher McDonald) is caught in the middle of an experiment gone horribly awry, he develops the power to leech the life force out of anyone he meets and becomes the villainous Hourglass. Intent on achieving immortality, the Hourglass attempts to gather as much life force as possible as the noble Dragonfly sets out to take down his archenemy and realize his destiny as a true hero. Craig Mazin writes and directs this low-flying spoof.<br /><br />featuring Tracy Morgan, Pamela Anderson, Leslie Nielsen, Marion Ross, Jeffrey Tambor, and Regina Hall.<br /><br />Hell Superhero Movie may earn some merit in the fact that it\\'s a hell of a lot better than Meet the Spartans and Epic Movie. But with great responsibility comes one of the worst outings of 2008 to date. Laughless but a little less irritating than Meet the Spartans. And in the same sense much more forgettable than meet the Spartans. But maybe that\\'s a good reason. There are still some of us trying to scrape away the stain that was Meet the Spartans from our memory.<br /><br />My final verdict? Avoid, unless you\\'re one of thoses people who enjoy such car crash cinema. As bad as Date Movie and Scary Movie 2 but not quite as bad as Meet the Spartans or Epic Movie. Super Villain.', shape=(), dtype=string)\n",
            "0.8604303\n",
            "\n",
            "tf.Tensor(b'Poor Shirley MacLaine tries hard to lend some gravitas to this mawkish, gag-inducing \"feel-good\" movie, but she\\'s trampled by the run-away sentimentality of a film that\\'s not the least bit grounded in reality.<br /><br />This was directed by Curtis Hanson? Did he have a lobotomy since we last heard from him? Hanson can do effective drama sprinkled with comedy, as evidenced by \"Wonder Boys.\" So I don\\'t know what happened to him here. This is the kind of movie that doesn\\'t want to accept that life is messy and fussy, and that neat, tidy endings (however implausible they might be) might make for a nice closing shot, but come across as utterly phony if the people watching the film have been through anything remotely like what the characters in the film go through.<br /><br />My wife and I made a game of calling out the plot points before they occurred -- e.g. \"the old man\\'s going to teach her to read and then drop dead.\" Bingo! This is one of those movies where the characters give little speeches summarizing their emotional problems, making you wonder why they still have emotional problems if they\\'re that aware of what\\'s causing them. Toni Collette (a fine actress, by the way, and one of my favorites if not given a lot to work with here), gives a speech early on about why she buys so many shoes and never wears them, spelling out in flashing neon the film\\'s awkward connecting motif. At that moment, I knew what I was in for, and the film was a downward spiral from there.<br /><br />Grade: C-', shape=(), dtype=string)\n",
            "0.0047751665\n",
            "\n",
            "tf.Tensor(b'As a former Erasmus student I enjoyed this film very much. It was so realistic and funny. It really picked up the spirit that exists among Erasmus students. I hope, many other students will follow this experience, too. However, I wonder if this movie is all that interesting to watch for people with no international experience. But at least one of my friends who has never gone on Erasmus also enjoyed it very much. I give it 9 out of 10.', shape=(), dtype=string)\n",
            "0.9683135\n",
            "\n",
            "tf.Tensor(b\"My God, Ryan Gosling has made a lot of deep characters in his career, this is one of his wonderful acting jobs. For me this is a very deep movie, needs a lot of concentration, not because is difficult to watch, just because you understand it if you put your shoes in this kid, even though has everything and has famous father that is a writer, has a deeper mind, you don't understand why he kills this poor kid, until you really heard what he has to say and you start to think, at least to me, that a lot of things that he says is true. Simple kid, sweet, very gentle, in a way normal like any teenage, but inside of him suffer because he start to look at the world in a different way, then you understand why he did what he did. I recommend this movie for those who likes deep drama.\", shape=(), dtype=string)\n",
            "0.6410631\n",
            "\n",
            "tf.Tensor(b\"This film just won the best film award at the Cleveland International Film Festival. It's American title apparently is Autumn Spring. The acting is superb. The story takes you into the life of an elderly man who takes what life deals him and spikes it up a little bit. Abetted by his best friend (and partner in not-so-serious crime) he puts people on at every opportunity but still often reveals his heart of gold. His longsuffering wife has come to her wits end and makes a life-changing decision which is heartbreaking to watch. The resolution of the story is beautiful.\", shape=(), dtype=string)\n",
            "0.9731204\n",
            "\n",
            "tf.Tensor(b'The cast for this production of Rigoletto is excellent. Edita Gruberova sings Gilda magnificently and passionately. Luciano Pavarotti also sings splendidly. Vergara is a fine Maddalena; Fedora Barbieri is a famous older singer who sings the maid, Giovanna. Weikl sings Marullo; Wixell sings both Rigoletto and Monterone. As Rigoletto, Wixell is probably the most convincing acting singer in this hard-to-beat ensemble of great singers. Kathleen Kuhlmann in the Contessa. All principals are well-known and world-renowned.<br /><br />This is an exciting Rigoletto visually as well as musically.<br /><br />I have it on both laser disc and DVD. You should have it too!', shape=(), dtype=string)\n",
            "0.9980109\n",
            "\n",
            "tf.Tensor(b'As long as you keep in mind that the production of this movie was a copyright ploy, and not intended as a serious release, it is actually surprising how not absolutely horrible it is. I even liked the theme music.<br /><br />And if ever a flick cried out for a treatment by Joel (or Mike) and the MST3K Bots, this is it! Watch this with a bunch of smart-ass wise-crackers, and you\\'re in for a good time. Have a brew, butter up some large pretzels, and enjoy.<br /><br />Of course, obtaining a copy requires buying a bootleg or downloading it as shareware, but if you\\'re here on the IMDb, then you\\'re most likely savvy enough to do so. Good luck.<br /><br />And look for my favorite part....where Dr. Doom informs the FF that they have 12 hours to comply with his wishes....and he actually gestures the number \"12\" with his finger while doing so....it\\'s like \"Evil Sesame Street\"....hoo boy.<br /><br />...and of course Mrs. Storm declaring \"Just look at you....the Fanstastic Four\" is just so heartwarming....you\\'ll laugh, you\\'ll cry.....<br /><br />So if you love schlocky Sci-Fi, this one\\'s Fantastic For you!', shape=(), dtype=string)\n",
            "0.03817576\n",
            "\n",
            "tf.Tensor(b\"Every great once in a while, you stumble upon a movie that exceeds even your wildest expectations. Given the IMDb rating of 4.0, I wasn't really expecting much with The Brotherhood of Satan. I hoped that at a minimum it might be cheesy fun like The Devil's Rain or any of the other early 70s similarly themed Satanic horror films. I couldn't' have been more wrong. What I got instead was an ambitious and intelligent film with a cast I really enjoyed. Speaking in broad terms to avoid giving anything away, the film's style and structure are much more experimental than the straightforward storytelling so prominent in the early 70s. The Brotherhood of Satan doesn't beat you over the head with plot points and explanations. A lot is left to the viewer to fill in the blanks. As a viewer, you know something is amiss, but for the longest period you're just not sure what it is. The unknown helps make for a far creepier atmosphere than most similar films. The ending is effective with its surreal imagery. I sat in amazement as the final credits began to roll. Those wanting a big slam-bang finale will be disappointed with the ending's simplicity. A lesser film would have tried to pull out all the stops and would, most likely, have failed miserably.<br /><br />There are moments in the film where it's easy to forget the director, Bernard McEveety, had primarily worked in television before The Brotherhood of Satan. There are a few scenes that are so well set-up, lit, and shot that even the most accomplished of directors could learn a thing or two. For example, I've seen enough films over the years to realize that directors can sometimes seem to have trouble shooting widescreen shots indoors. Not here. The scene where the men are discussing their plan of action in the sheriff's office is amazing. We see all five men at once \\xc2\\x96 each doing their own thing as in real life. In a lesser film, we might see all the men at once, but each would be motionless, quietly waiting their turn to deliver their dialogue. It's a small scene, but it looks so natural and is so beautifully shot that it's one of my favorite moments of The Brotherhood of Satan.<br /><br />Finally, I mentioned the acting in my opening, so without going into a long-winded speech, I'll just say that The Brotherhood of Satan features Strother Martin and L.Q. Jones. Any film with these two guys is almost an automatic winner with me.\", shape=(), dtype=string)\n",
            "0.4049095\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlc4tsBbMNdw",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
      ]
    }
  ]
}